Heuristic quantum genetic method of multi-target distribution in air war

Abstract

The invention provides a heuristic quantum genetic method of multi-target distribution in an air war and belongs to the technical field of computer simulation and method optimization. The heuristic quantum genetic method comprises the following steps: obtaining the current battlefield situation from a command control center; obtaining threat factors among aircrafts between us and the enemy of the current battlefield; obtaining all of attack distribution values of each weapon of the aircrafts of ourselves and establishing a priority attack distribution value vector; carrying out quantum bit encoding and initializing all quantum chromosomes in a population; filtering the quantum chromosomes; and correcting the quantum chromosomes according to the priority attack distribution value vector. According to the heuristic quantum genetic method, a threat empirical formula of collaborative multi-target attack air war decision problem is subjected to deformation conversion, a distribution scheme of the weapons of us is subjected to quantum bit encoding, and the representation range of feasible solution is enlarged. The priority attack distribution value vector PAVZN*1 is provided and designed according to all of the attack distribution values of each weapon, thus the quantum chromosomes are corrected in a heuristic mode according to the PAVZN*1, and the convergence velocity is accelerated.

Description

technical field [0001] The invention relates to a heuristic quantum genetic method for multi-target allocation in air combat, and belongs to the technical field of computer simulation and method optimization. Background technique [0002] Coordinated multi-target attack air combat decision-making has become one of the key technologies for modern fighters to achieve beyond visual range air combat fire control system, and its research is of great significance. Coordinated multi-target attack air combat decision-making refers to an aircraft alone or multiple aircraft simultaneously attacking multiple enemy scattered targets in the air. When the number of enemy aircraft is large, we also need to dispatch multiple aircraft to intercept and attack them at the same time, thus forming a coordinated air battle. The key to the decision-making of coordinated multi-target attack air combat is to assign targets to friendly aircraft according to the number of our aircraft and the situati...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems existing in the prior art, the present invention proposes a heuristic quantum genetic method for air combat multi-target allocation, the purpose is to solve the problem that the genetic method of cooperative target allocation for cooperative multi-target attack air combat decision-making is easy to local convergence, and the convergence speed in the later stage of evolution Slow, poor accuracy and other shortcomings, transform the threat experience formula of cooperative multi-target attack air combat decision-making problem, so that it can solve our weapon allocation plan (here, each chromosome in the heuristic quantum genetic algorithm represents a solution scheme) to encode qubits, and propose a priority assignment value vector PAV ZN×1 The heuristic quantum chromosome correction method, and finally through the quantum chromosome mutation, accelerates the rotation of the qubit corresponding state to the global optimal solution and improves the search efficiency

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